1. Field of the Invention
The present invention generally relates to the field of precision irrigation for agriculture and more specifically to precision irrigation that is based upon forecasted crop water needs and additional factors.
2. Background
The challenge for modern irrigated agriculture is to provide acceptable yields while conserving water and energy and to perform this conservation at a scale large enough to be meaningful: that is, across large farmed geographic areas. To be an effective solution, a system and method must combine remote sensing data to assess the actual condition of the crop canopy as it relates to its water use and spatially-variable climatic data as it further adjusts the crop's water usage. For irrigation control across regional scales that encompass thousands of square miles, a solution must address at least three types of data necessary to accurately track crop irrigation requirements in time and space: (1) the condition of the crop canopy and how it affects crop water use in a field and the regions within the field that must be known at all times during the growing season for each of potentially thousands of fields across the geographic area; (2) the spatial and temporal variability of reference evapotranspiration (ET) across the geographic area, a mathematical representation of the driving force for ET directly influenced by weather; and (3) rainfall that differentially offsets irrigation requirements and that varies across the geographic area.
Next, for guiding irrigation, three operations must be performed at regular periods for each field and its regions, preferably daily: crop irrigation requirements must be determined; the irrigation prescription must be delivered remotely; and data must be gathered from individual fields for feedback to the irrigation calculations, for example rainfall, reference ET, and successful irrigation.
Systems that adjust irrigation or provide remote irrigation control through the Internet and wireless connectivity are well known in the art. These systems may utilize ET estimation, meteorological, and other data that may be useful in irrigation control but fall short of the large scale that is needed for maximum water conservation. For example, U.S. Pat. No. 5,696,671 “Evapotranspiration Forecasting Irrigation Control System” provides a method and apparatus for using predictive evapotranspiration (ET) and precipitation data in controlling an irrigation system. That system provides for adjusting ET values based upon forecasted weather and using these forecasts to adjust a watering schedule. However, the system fails to describe how ET for the plant cover is determined; it shows only how it is to be adjusted for rain that is received or changes in the atmospheric driving force for ET from that forecasted. That system fails to provide spatial adjustment of the parameters for calculating crop irrigation, both in terms of the sensitivity to crop canopy variability and its influence on water use, or application of reference ET often used as a scalar for the water use of the crop canopy, nor rainfall that offsets each field's irrigation requirement.
U.S. Pat. No. 5,870,302, “Evapotranspiration Remote Irrigation Control System” also discloses a method and system using ET data and monitoring meteorological data used to adjust wet watering schedules based upon meteorologic and ET data. However, the system is not sensitive to variability in the crop canopy and the phenologic timing for crop canopy development that exerts a controlling influence upon crop water use—phenology may vary widely across an agricultural geographic area. Neither does the system specify how to assess the actual water use (ET) of the crop, a necessary step when agricultural crops may be planted and emerge through a two month window, thus influencing large differences in the crop's water use. Neither does the system address the spatial variability of the weather as it affects the crop's water status, nor the remote connectivity necessary for serving the irrigation control of the system and method.
U.S. Pat. No. 6,782,311, “Remotely Controlled Irrigation Timer with Fault Detection”, provides irrigation scheduling based on microclimate weather data but simply adjusts automated irrigation schedules served by timer, doing so by neighborhoods rather than continuously varying across agricultural geographic areas encompassing a much larger scale. The adjustment of watering takes place in this system through delaying or expediting watering according to a schedule set in advance; it contains no remote assessment of plant canopy conditions that can be used to assess irrigation needs.
Internet-based methods and apparatus for remotely controlling irrigation systems are exemplified in U.S. Pat. No. 7,587,053 B2 “Universal Remote Terminal, Unit and Method for Tracking the Position of Self-Propelled Irrigation Systems.” The method and apparatus provides the machinery for tracking the pivot and controlling the irrigation system that can turn the sprinkler system on and off and can be operated remotely from the field.
The water use of a crop is directly dependent upon the greenness and development of the canopy. Greenness is assessed in the prior art using remote sensing and Earth observation satellite (EOS) data. The condition of the crop canopy assessed using EOS data is used as a scalar against reference ET in the same manner as K factors that are often used for estimating irrigation in agriculture as part of the prior art. K factors are generally derived empirically, published for each crop type, and based upon the assumed growth stage of the crop, rather than on an actual measure of the crop's development.
In the prior art, the dual crop coefficient method is a commonly used standard agricultural method that contains two empirically-derived K factors that partition actual crop water use (designated ETa, herein) into proportions of its water transpired and intercepted by the canopy and that portion evaporated from the soil, that when multiplied by reference ET, provide an estimate of ETa. Reference ET, designated ETr here, is calculated by published and standardized methods to express the evaporation power of the atmosphere acting upon tall-statured crops. This index has units of depth, commonly millimeters or inches per time, often daily. Other published and standardized ET reference indices could similarly work for such calculations in the prior art, for example ETo, commonly used for a short statured crop such as grass. Through calibration, these two indices are interchangeable to derive the same ETa estimate.ETa=ETr·(Kcb+Ke)  Equation 1                Where Kcb is a basal crop coefficient expressing canopy water losses, Ke is a coefficient expressing evaporation of water from the soil surface. ETr is the reference ET applicable for tall-statured crops.        
Use of remotely-sensed vegetation indices to express the actual greenness of a crop canopy for the Kcb of Equation 1 is an enhancement of the dual crop coefficient method. Crop greenness is portrayed by vegetation indices that combine red and near infrared light. Crop canopies reflect highly in the near infrared, as do many background surfaces, a common example being dry soils, while the red light is absorbed for photosynthesis. The ratio of red versus near infrared reflectance creates highly useful indices of plant activity that are inversely proportional to the red signal. The normalized difference vegetation index (NDVI; Equation 2) is the most commonly used among these indices.
                    NDVI        =                              NIR            -            Red                                NIR            +            Red                                              Eqaution        ⁢                                  ⁢        2                            Where NIR is the near infrared band and Red is the red band of digital data commonly measured by sensors borne by either aircraft or Earth Observing Satellite (EOS) platforms.        
As an estimator of canopy greenness, NDVI accuracy is compromised by complex factors related to soil background and atmospheric aerosol effects that scatter and diffuse light. In combination, these influences attenuate the vegetation signal that NDVI is designed to measure. The accuracy for NDVI to portray hydrologic responses is enhanced by conversion to NDVI* that is calibrated to represent the full range of vegetation greenness from none, given the value zero, to saturated greenness, given the value one. The NDVI* index has been shown in the prior art to out-perform all other commonly used vegetation indices for tracking the hydrologic signal in plants (Baugh and Groeneveld, 2006). Calculation of NDVI* corrects for the error-inducing effects from soil background and atmospheric aerosols (Equation 3).
                              NDVI          *                =                                            NDVI              i                        -                          NDVI              0                                                          NDVI              S                        -                          NDVI              0                                                          Equation        ⁢                                  ⁢        3                            Where NDVIi is the measured NDVI for the ith pixel, NDVIs is the saturated value for NDVI, and NDVI0 is the NDVI value representing bare soil.        
NDVI* is calibrated by measuring NDVI0 for a field bare of vegetation cover or by using cumulative distribution functions for NDVI and hence, requires analysis of the scene for calibration with no specific ground target or ground-based measurements. NDVI* can be calculated from any EOS data, such as Landsat Thematic Mapper, that includes red and near infrared broad bands.
For comparison of EOS data across seasons, the effect of variable solar angles and distance to the sun are corrected through the calculation of reflectance with methods that are standardized and widely published. NDVI* functions as an approximation of at-ground NDVI and also corrects for the influence of the bare soil background that generally is above zero NDVI. Particularly during the early growing season, the exposed soil background may have a controlling relationship for the obtained NDVI values since the soil is completely or partially visible to the view from the EOS platform.
Since NDVI* represents a range of water use from zero to one, it is analogous to the distribution of ETr, that also ranges from zero to some energy-limited maximal value. NDVI* is used as a scalar value to adjust ETr to achieve first-order estimates of ETa. Such first order estimates are appropriate for many vegetation types as shown in papers by Groeneveld and Baugh (2007) and Groeneveld et al. (2007) that examine how to process the EOS data and then use it to make estimates of ETa.
NDVI* is an accurate surrogate for Kcb in Equation 1 because it is an expression of the degree of gas exchange that is occurring in the canopy. Chlorophyll is an expensive molecule for plants to manufacture metabolically and so, the canopy optimizes the chlorophyll it produces. During photosynthesis pores within the leaves open to enable gas exchange with the atmosphere, enabling the uptake of carbon dioxide and combination with water molecules split by energy using red light. Photosynthesis is moderated by chlorophyll and the resulting carbohydrate building blocks for plant tissue are a measure of crop productivity. When the pores of the leaves are open, water molecules also evaporate from the water-saturated tissues inside. Hence, chlorophyll, photosynthesis, gas exchange and water loss are directly linked and this linkage forms the basis for estimating crop water use within this embodiment of the invention.
Crops rely upon stored soil water between rainfall and irrigation events and this storage is the key for all rational irrigation control. Accurate estimation of crop water use enables irrigation to simply replace the water that the crop has extracted from the soil as the rational basis for scheduling irrigation.